Nevin Manimala

Br J Surg. 2025 Apr 30;112(5):znaf082. doi: 10.1093/bjs/znaf082.

ABSTRACT

BACKGROUND: Implant-based breast reconstruction is the most common technique after mastectomy. Breast implants are categorized by surface type as smooth, textured, or polyurethane-covered, each with specific attributed advantages and complication profiles. High-quality comparative studies are, however, limited. This study compared revision incidence and indications for revision among these implant types.

METHODS: A prospective, nationwide cohort from the Dutch Breast Implant Registry was analysed. Permanent implants used between 2017 and 2022 for direct-to-implant or two-stage reconstruction were included. Surface-related revision was the primary outcome. Cumulative incidences were estimated using a competing risk model. Cause-specific hazard ratios (HRcs) were calculated using univariable and multivariable models, accounting for implant clustering and confounders. Subgroup analyses examined revisions for specific complications.

RESULTS: Of 3996 implants, 76.9% were textured, 12.4% smooth, and 10.8% polyurethane-covered. At 4 years, the cumulative incidence of revision surgeries did not differ between textured (11.1%; 95% c.i. = 9.9 to 12.5), smooth (13.0%; 95% c.i. = 8.5 to 18.4), and polyurethane-covered (16.1%; 95% c.i. = 12.4 to 20.2) implants. Multivariable analysis found no association between surface type and surface-related revision. Subgroup analysis however revealed that polyurethane-covered implants had increased hazards of revision for capsular contracture (HRcs = 2.49; 95% c.i. = 1.24 to 5.01) and asymmetry (HRcs = 2.31; 95% c.i. = 1.33 to 4.02).

CONCLUSION: After adjusting for confounders and clustering, surface-related revision in a reconstructive setting did not significantly different among breast implant surface types overall. Polyurethane-covered implants may, however, require more revisions due to capsular contracture and asymmetry.

PMID:40380859 | DOI:10.1093/bjs/znaf082

J Gerontol B Psychol Sci Soc Sci. 2025 May 17:gbaf089. doi: 10.1093/geronb/gbaf089. Online ahead of print.

ABSTRACT

OBJECTIVES: Mortality prediction and the identification of mortality risks are central to social and biological sciences. Traditional models often assess linear associations between single risk factors and mortality. Transformer models, capable of capturing long-term dependencies across multiple variables, offer a novel approach to mortality prediction. This study introduces a transformer-based model applied to data from the Health and Retirement Study (HRS).

METHODS: We analyzed data provided by 38,193 adults aged ≥50 years participating in the HRS, a longitudinal US study surveyed biennially since 1992. Linked mortality data were obtained from the National Death Index and postmortem interviews. Using the transformer architecture, we modeled changes in 126 risk factors spanning financial, physical, and mental health domains manifesting over 29 years. Prediction performance was assessed across multiple settings, with traditional statistical and machine learning models serving as benchmarks.

RESULTS: Over a median follow-up of 9 years, 17,448 deaths occurred (crude rate: 39.6 per 1,000 person-years). The transformer model consistently outperformed traditional and machine learning methods, achieving a twofold improvement in average precision scores (APS) for next-wave mortality prediction relative to the best benchmark model.

DISCUSSION: Transformer-based models, such as BEHRT, significantly enhance mortality prediction compared with traditional approaches. These findings highlight the potential of transformer neural network models in social science-focused population health research on aging.

PMID:40380823 | DOI:10.1093/geronb/gbaf089

BJS Open. 2025 May 7;9(3):zraf030. doi: 10.1093/bjsopen/zraf030.

ABSTRACT

BACKGROUND: Operating rooms generate substantial waste and budget expenditure due to extensive material usage. Reusable instruments are often packaged in trays, which accumulate instruments over time. This review quantifies the advantages of tray optimization (removing redundant instruments), including reduced environmental impact, costs, operating room and processing time.

METHODS: Following PRISMA guidelines, searches were conducted in PubMed, Embase, Web of Science and The Cochrane Library in August 2024 for studies on optimizing surgical trays in human surgeries. Studies were included if they reported on optimization approaches and outcomes related to environmental, economic or efficiency improvements. Exclusions included studies on disposable instruments, animal or veterinary research and patient-specific trays. Risk of bias was assessed using the ROBINS-I (Risk Of Bias In Non-randomised Studies – of Interventions) tool.

RESULTS: The search identified 4511 studies, with 45 meeting the inclusion criteria. Half of the studies showed a serious risk of bias, while the rest had a moderate risk. Three main optimization strategies were identified, with expert analysis being the most common (n = 29), followed by mathematical modelling. Environmental benefits were reported in all three included studies, although limited in number. Studies reported that 19 to 89% of instruments could be removed from trays, with 31 studies unanimously reporting cost reductions. Additionally, 17 studies demonstrated improved operational efficiency.

CONCLUSION: Tray optimization strategies effectively reduce resource use, resulting in environmental and economic benefits. Although no standard method exists, effective strategies such as procedure observation and clinician feedback may eliminate over half of the instruments, offering a significant opportunity to minimize resource consumption in the operating room.

PMID:40380812 | DOI:10.1093/bjsopen/zraf030

Global Spine J. 2025 May 16:21925682251343525. doi: 10.1177/21925682251343525. Online ahead of print.

ABSTRACT

Study DesignA systematic review and meta-analysis.ObjectivesThis systematic review and meta-analysis aim to evaluate the diagnostic accuracy of CT in detecting PLC injuries in traumatic thoracolumbar fractures.MethodsA comprehensive search of PubMed/MEDLINE, Embase, and Web of Science was conducted up to January 2025. Studies were included if they examined the diagnostic validity of CT for PLC injuries compared with MRI with predefined outcomes (true/false positives/negatives). Quality assessment was performed using the QUADAS-2 tool, and statistical analysis involved bivariate binomial regression to generate summary receiver operating characteristic (SROC) curves and pooled estimates of sensitivity and specificity.ResultsEight studies involving 1440 patients were included. The pooled sensitivity and specificity of CT for PLC injury detection were 75% (95% CI: 68 to 80, P = 0.00) and 87% (95% CI: 71 to 95, P = 0.00), respectively. The area under the curve (AUC) from the SROC analysis was 0.81 (95% CI: 0.78 to 0.84), indicating fair diagnostic accuracy. Meta-regression analysis revealed that sensitivity and specificity remained consistent across advanced CT techniques, multiplanar reconstruction, and full MRI protocol, but extensive trauma, CT 16-64 or ≥128 slices, and 3.0 T MRI scanner influenced it. No significant publication bias was detected.ConclusionThis meta-analysis demonstrates that CT has fair diagnostic accuracy for detecting PLC injuries in traumatic thoracolumbar fractures, supporting its clinical utility. Future research should explore integrating advanced imaging technologies to enhance CT’s diagnostic precision.

PMID:40380790 | DOI:10.1177/21925682251343525

Stud Health Technol Inform. 2025 May 15;327:1471-1472. doi: 10.3233/SHTI250655.

ABSTRACT

This study aimed to explore nursing care for patients underwent coronary artery bypass graft (CABG) surgery using standardized nursing records. A total of 184,029 nursing statements from 122 patients were analyzed, with 437 unique statements accounting for the majority of data. These statements were semantically mapped to SNOMED CT concepts related to clinical findings, situations, and procedures. The most prominent categories included pain management, postoperative lung care, and mental status assessments. The findings highlight the potential of standardized nursing records in identifying key nursing practices and improving outcomes for CABG surgery patients.

PMID:40380758 | DOI:10.3233/SHTI250655

Stud Health Technol Inform. 2025 May 15;327:1457-1458. doi: 10.3233/SHTI250648.

ABSTRACT

This research explores factors influencing the use of wearable technology by people with Multiple Sclerosis (PwMS) to assist in symptom self-management. Using Q-methodology, 19 PwMS in Saudi Arabia (SA) participated. The PwMS study revealed three perspectives: supporters of wearable technology, pragmatic optimists, and cautious sceptics. The preliminary findings highlight the importance of addressing both practical and psychological barriers to facilitate wearables integration in the management of MS.

PMID:40380751 | DOI:10.3233/SHTI250648

Stud Health Technol Inform. 2025 May 15;327:1451-1452. doi: 10.3233/SHTI250645.

ABSTRACT

This research evaluates ICD-11 using the cancer Biomedical Informatics Grid (caBIG®) Terminology Review Criteria Matrix version 3.3 and the National Committee on Vital and Health Statistics (NCVHS) criteria for adoption and implementation and guidelines for curation and dissemination of health terminology and vocabulary standards. The aim is to determine if ICD-11 meets acceptable terminology practices and to identify its strengths and weaknesses.

PMID:40380748 | DOI:10.3233/SHTI250645

Stud Health Technol Inform. 2025 May 15;327:1447-1448. doi: 10.3233/SHTI250643.

ABSTRACT

This paper explores and analyzes a high-frequency vital sign- and event dataset from surgical ward patients to prepare for the training and application of predictive models.

PMID:40380746 | DOI:10.3233/SHTI250643

Stud Health Technol Inform. 2025 May 15;327:1413-1417. doi: 10.3233/SHTI250635.

ABSTRACT

Depression is one of the most prevalent mental health illnesses and a significant public health concern in Saudi Arabia. Due to misconceptions about mental health diseases, such as depression, in Saudi Arabia, there is widespread stigma. Many people with depression, therefore, seek health information via social media such as blogs, microblogs, and online communities. Online mental health communities (OMHCs) have been developed only recently in the country. This study explores the challenges people experience when engaging in OMHCs. A sample of 1,422 posts was generated from two OMHCs and analyzed using inductive thematic analysis. Three main themes were identified: misinformation, triggering vulnerability, and judgment. Findings from this study will be shared with the OMHCs and will help administrators to develop strategies and policies to enhance the experience of people with depression in using OMHCs.

PMID:40380738 | DOI:10.3233/SHTI250635

Stud Health Technol Inform. 2025 May 15;327:1403-1407. doi: 10.3233/SHTI250633.

ABSTRACT

In this study, we attempted to identify the subtypes of autism spectrum disorder (ASD) with the help of anatomical alterations found in structural magnetic resonance imaging (sMRI) data of the ASD brain and machine learning tools. Initially, the sMRI data was preprocessed using the FreeSurfer toolbox. Further, the brain regions were segmented into 148 regions of interest using the Destrieux atlas. Features such as volume, thickness, surface area, and mean curvature were extracted for each brain region. We performed principal component analysis independently on the volume, thickness, surface area, and mean curvature features and identified the top 10 features. Further, we applied k-means clustering on these top 10 features and validated the number of clusters using Elbow and Silhouette method. Our study identified two clusters in the dataset which significantly shows the existence of two subtypes in ASD. We identified the features such as volume of scaled lh_G_front middle, thickness of scaled rh_S_temporal transverse, area of scaled lh_S_temporal sup, and mean curvature of scaled lh_G_precentral as the significant features discriminating the two clusters with statistically significant p-value (p<0.05). Thus, our proposed method is effective for the identification of ASD subtypes and can also be useful for the screening of other similar neurological disorders.

PMID:40380736 | DOI:10.3233/SHTI250633